29th October 2021
Single Cell RNAseq Analysis Workflow
Quality Control
We will now check the quality of the data further:
- Mapping quality
- Cell counts
- Distribution of keys quality metrics
We will then:
- Filter genes with very low expression
- Identify low-quality cells
- Filter and/or mark low quality cells
Quality Control
We will now check the quality of the data further:
- Mapping quality
- Cell counts
- Distribution of keys quality metrics
We will then:
- Filter genes with very low expression
- Identify low-quality cells
- Filter and/or mark low quality cells
Quality Control
Bioconductor R packages:
- scater
- DropletUtils
Orchestrating Single-Cell Analysis with Bioconductor Robert Amezquita, Aaron Lun, Stephanie Hicks, Raphael Gottardo
Read CellRanger outputs into R
Loading a single sample
sample.path <- c(SRR9264343 = "CellRanger_Outputs/SRR9264343/outs/filtered_feature_bc_matrix/") sce <- read10xCounts(sample.path, col.names=TRUE)
Loading multiple samples
list_of_files <- c(SRR9264343 = "CellRanger_Outputs/SRR9264343/outs/filtered_feature_bc_matrix",
SRR9264344 = "CellRanger_Outputs/SRR9264344/outs/filtered_feature_bc_matrix",
SRR9264347 = "CellRanger_Outputs/SRR9264347/outs/filtered_feature_bc_matrix")
sce <- read10xCounts(sample.path, col.names=TRUE)
\(\rightarrow\) SingleCellExperiment object
The SingleCellExperiment object
The Counts Matrix
The Counts Matrix
counts(sce)
## 36601 x 3094 sparse Matrix of class "dgCMatrix"
## [[ suppressing 44 column names 'AAACCTGAGACTTTCG-1', 'AAACCTGGTCTTCAAG-1', 'AAACCTGGTGCAACTT-1' ... ]] ## [[ suppressing 44 column names 'AAACCTGAGACTTTCG-1', 'AAACCTGGTCTTCAAG-1', 'AAACCTGGTGCAACTT-1' ... ]]
## ## ENSG00000175756 1 2 . 1 1 . . 1 . . . 2 1 . 1 2 2 1 . . 1 . 1 3 1 1 2 1 2 2 . 6 . . 1 1 1 . . . 1 3 1 . ...... ## ENSG00000221978 . 2 . . . . . . . . 3 . . . . 1 . . . . . . . . 1 . . . . 1 . . . . . 1 . . . . 1 . . . ...... ## ENSG00000224870 . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000242485 1 2 . 1 2 . . 1 . 1 1 4 . . 1 . 1 2 . 2 . 1 2 . . . . . 1 . . 2 2 . . 1 1 1 1 . 1 . 1 . ...... ## ENSG00000272455 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000235098 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000225905 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000205116 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000225285 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000179403 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000215915 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160072 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000197785 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . ...... ## ENSG00000205090 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160075 2 1 . . . . 1 3 1 4 2 3 1 . . . 1 1 . 2 2 1 . 4 2 1 1 1 . . 1 2 . . . 1 . . 1 . 1 2 . . ...... ## ENSG00000215014 . . . . . . . . 1 . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000228594 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000286989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000272106 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000197530 . . . . . . . . . . . . . . . 1 . . . . 1 . . . . . . . . . . 1 . . 1 . . . . . . . . . ...... ## ENSG00000189409 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000248333 . 1 . . . . . . 1 . 2 . . . . . . . . . 1 1 . . . . . . . . . . . . . . 1 . 1 . . . . . ...... ## ENSG00000272004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ## .............................. ## ........suppressing 3050 columns and 36556 rows in show(); maybe adjust 'options(max.print= *, width = *)' ## ..............................
## [[ suppressing 44 column names 'AAACCTGAGACTTTCG-1', 'AAACCTGGTCTTCAAG-1', 'AAACCTGGTGCAACTT-1' ... ]]
## ## ENSG00000285812 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000223823 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000272141 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000205231 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162571 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000186891 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000186827 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000078808 . . . . . . 1 . . . . . . . . . . . . 2 . . . . . 1 . . . . . 2 . . . . . . . . . . . . ...... ## ENSG00000176022 . . . . 1 . 1 . . 1 . . . . . . 1 . 1 . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000184163 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000260179 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160087 . 1 . 1 . . . 1 1 1 1 1 . . . 1 1 1 . 1 . . 1 . . . . . 1 . . 2 . . . . . . . . . 1 2 . ...... ## ENSG00000230415 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162572 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000131584 . . . . . 1 . 2 . . . . . . . . . . . . . 1 . . . 1 . . . . . . . . 1 1 . . . . . . 1 . ...... ## ENSG00000169972 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000127054 1 1 . 1 . . . 2 . . 1 1 . . 2 . . . . . . . 1 . . . . . 1 1 . . . . . . . . . . . . 1 . ...... ## ENSG00000240731 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000224051 . 1 . 1 . . . . . . . 1 . . . . . . 1 . . . . . . 1 . . . . . . . . . . . . . . . . . . ...... ## ENSG00000169962 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000107404 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162576 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......
Feature metadata
Feature metadata
rowData(sce)
## DataFrame with 36601 rows and 3 columns ## ID Symbol Type ## <character> <character> <character> ## ENSG00000175756 ENSG00000175756 AURKAIP1 Gene Expression ## ENSG00000221978 ENSG00000221978 CCNL2 Gene Expression ## ENSG00000224870 ENSG00000224870 MRPL20-AS1 Gene Expression ## ENSG00000242485 ENSG00000242485 MRPL20 Gene Expression ## ENSG00000272455 ENSG00000272455 AL391244.2 Gene Expression ## ... ... ... ... ## ENSG00000240731 ENSG00000240731 AL139287.1 Gene Expression ## ENSG00000224051 ENSG00000224051 CPTP Gene Expression ## ENSG00000169962 ENSG00000169962 TAS1R3 Gene Expression ## ENSG00000107404 ENSG00000107404 DVL1 Gene Expression ## ENSG00000162576 ENSG00000162576 MXRA8 Gene Expression
Droplet annotation (Cell metadata)
Droplet annotation (Cell metadata)
colData(sce)
## DataFrame with 3094 rows and 2 columns ## Sample Barcode ## <character> <character> ## AAACCTGAGACTTTCG-1 SRR9264343 AAACCTGAGACTTTCG-1 ## AAACCTGGTCTTCAAG-1 SRR9264343 AAACCTGGTCTTCAAG-1 ## AAACCTGGTGCAACTT-1 SRR9264343 AAACCTGGTGCAACTT-1 ## AAACCTGGTGTTGAGG-1 SRR9264343 AAACCTGGTGTTGAGG-1 ## AAACCTGTCCCAAGTA-1 SRR9264343 AAACCTGTCCCAAGTA-1 ## ... ... ... ## TTTGGTTTCTTTAGGG-1 SRR9264343 TTTGGTTTCTTTAGGG-1 ## TTTGTCAAGAAACGAG-1 SRR9264343 TTTGTCAAGAAACGAG-1 ## TTTGTCAAGGACGAAA-1 SRR9264343 TTTGTCAAGGACGAAA-1 ## TTTGTCACAGGCTCAC-1 SRR9264343 TTTGTCACAGGCTCAC-1 ## TTTGTCAGTTCGGCAC-1 SRR9264343 TTTGTCAGTTCGGCAC-1
colnames(counts(sce))
## [1] "AAACCTGAGACTTTCG-1" "AAACCTGGTCTTCAAG-1" "AAACCTGGTGCAACTT-1" "AAACCTGGTGTTGAGG-1" ## [5] "AAACCTGTCCCAAGTA-1" "AAACCTGTCGAATGCT-1" "AAACGGGCACCATCCT-1" "AAACGGGCAGACGTAG-1" ## [9] "AAACGGGCAGTTCATG-1" "AAACGGGGTAAGCACG-1" "AAACGGGGTTCACCTC-1" "AAACGGGTCGGTTCGG-1" ## [13] "AAACGGGTCGTGGTCG-1" "AAAGATGAGCGATGAC-1" "AAAGATGCAAATACAG-1" "AAAGATGCAAGCTGAG-1" ## [17] "AAAGATGCAGCCAATT-1" "AAAGATGTCTCCCTGA-1" "AAAGCAAAGACTTGAA-1" "AAAGCAAAGGCTAGAC-1" ## [21] "AAAGCAAAGTCCAGGA-1" "AAAGCAAAGTCCGGTC-1"
Properties of RNAseq data - Number of genes detected per cell
Properties of RNAseq data - Total UMIs
Properties of RNAseq data - Distribution of counts for a gene across cells
Properties of RNAseq data - Distribution of counts for a gene across cells
Quality Control
- Not all of the droplets called as cells by CellRanger will contain good quality cells
- Poor quality droplets will adversely affect downstream analysis
- We can use QC metrics to filter out poor quality droplets:
- Total UMIs (Library size)
- Number of gene detected
- Proportion of UMIs mapping to mitochondrial genes
- Total UMIs (Library size)
Quality Control
- Add gene annotation to identify Mt genes -
AnnotationHub
rowData(sce)
## DataFrame with 36601 rows and 4 columns ## ID Symbol Type Chromosome ## <character> <character> <character> <character> ## ENSG00000175756 ENSG00000175756 AURKAIP1 Gene Expression 1 ## ENSG00000221978 ENSG00000221978 CCNL2 Gene Expression 1 ## ENSG00000224870 ENSG00000224870 MRPL20-AS1 Gene Expression 1 ## ENSG00000242485 ENSG00000242485 MRPL20 Gene Expression 1 ## ENSG00000272455 ENSG00000272455 AL391244.2 Gene Expression 1 ## ... ... ... ... ... ## ENSG00000212907 ENSG00000212907 MT-ND4L Gene Expression MT ## ENSG00000198886 ENSG00000198886 MT-ND4 Gene Expression MT ## ENSG00000198786 ENSG00000198786 MT-ND5 Gene Expression MT ## ENSG00000198695 ENSG00000198695 MT-ND6 Gene Expression MT ## ENSG00000198727 ENSG00000198727 MT-CYB Gene Expression MT
Quality Control
is.mito <- which(rowData(sce)$Chromosome=="MT") sce <- addPerCellQC(sce, subsets = list(Mito = is.mito))
Adds six columns to the droplet annotation:
- sum: total UMI count
- detected: number of features (genes) detected
- subsets_Mito_sum: number of UMIs mapped to mitochondrial transcripts
- subsets_Mito_detected: number of mitochondrial genes detected
- subsets_Mito_percent: percentage of UMIs mapped to mitochondrial transcripts
- total: also the total UMI count
Quality Control
is.mito <- which(rowData(sce)$Chromosome=="MT") sce <- addPerCellQC(sce, subsets = list(Mito = is.mito))
colData(sce)
## DataFrame with 3094 rows and 8 columns ## Sample Barcode sum detected subsets_Mito_sum #### AAACCTGAGACTTTCG-1 SRR9264343 AAACCTGAGACTTTCG-1 6677 2056 292 ## AAACCTGGTCTTCAAG-1 SRR9264343 AAACCTGGTCTTCAAG-1 12064 3177 575 ## AAACCTGGTGCAACTT-1 SRR9264343 AAACCTGGTGCAACTT-1 843 363 428 ## AAACCTGGTGTTGAGG-1 SRR9264343 AAACCTGGTGTTGAGG-1 8175 2570 429 ## AAACCTGTCCCAAGTA-1 SRR9264343 AAACCTGTCCCAAGTA-1 8638 2389 526 ## ... ... ... ... ... ... ## TTTGGTTTCTTTAGGG-1 SRR9264343 TTTGGTTTCTTTAGGG-1 3489 1600 239 ## TTTGTCAAGAAACGAG-1 SRR9264343 TTTGTCAAGAAACGAG-1 7809 2415 548 ## TTTGTCAAGGACGAAA-1 SRR9264343 TTTGTCAAGGACGAAA-1 9486 2589 503 ## TTTGTCACAGGCTCAC-1 SRR9264343 TTTGTCACAGGCTCAC-1 1182 591 224 ## TTTGTCAGTTCGGCAC-1 SRR9264343 TTTGTCAGTTCGGCAC-1 10514 2831 484 ## subsets_Mito_detected subsets_Mito_percent total ## ## AAACCTGAGACTTTCG-1 12 4.37322 6677 ## AAACCTGGTCTTCAAG-1 12 4.76625 12064 ## AAACCTGGTGCAACTT-1 11 50.77106 843 ## AAACCTGGTGTTGAGG-1 12 5.24771 8175 ## AAACCTGTCCCAAGTA-1 13 6.08937 8638 ## ... ... ... ... ## TTTGGTTTCTTTAGGG-1 11 6.85010 3489 ## TTTGTCAAGAAACGAG-1 12 7.01754 7809 ## TTTGTCAAGGACGAAA-1 12 5.30255 9486 ## TTTGTCACAGGCTCAC-1 11 18.95093 1182 ## TTTGTCAGTTCGGCAC-1 12 4.60339 10514
QC metrics - distribution
plotColData(sce, x="Sample", y="sum") + scale_y_log10() plotColData(sce, x="Sample", y="detected") + scale_y_log10() plotColData(sce, x="Sample", y="subsets_Mito_percent")
QC metrics - relationship
Identification of low-quality cells with adaptive thresholds
sce$low_lib_size <- isOutlier(sce$sum, log=TRUE, type="lower") sce$low_n_features <- isOutlier(sce$detected, log=TRUE, type="lower") sce$high_Mito_percent <- isOutlier(sce$subsets_Mito_percent, type="higher")
All three filter steps at once
cell_qc_results <- quickPerCellQC(colData(sce), percent_subsets=c("subsets_Mito_percent"))
## DataFrame with 3094 rows and 4 columns ## low_lib_size low_n_features high_subsets_Mito_percent discard ## <outlier.filter> <outlier.filter> <outlier.filter> <logical> ## 1 FALSE FALSE FALSE FALSE ## 2 FALSE FALSE FALSE FALSE ## 3 TRUE TRUE TRUE TRUE ## 4 FALSE FALSE FALSE FALSE ## 5 FALSE FALSE FALSE FALSE ## ... ... ... ... ... ## 3090 FALSE FALSE FALSE FALSE ## 3091 FALSE FALSE FALSE FALSE ## 3092 FALSE FALSE FALSE FALSE ## 3093 FALSE TRUE TRUE TRUE ## 3094 FALSE FALSE FALSE FALSE
All three filter steps at once
cell_qc_results <- quickPerCellQC(colData(sce), percent_subsets=c("subsets_Mito_percent"))
Filter the Single Cell Object
- Filter cells according to QC metrics.
- Additionally, filter out uninformative genes.
undetectedGenes <- rowSums(counts(sce)) == 0 sce.Filtered <- sce[!undetectedGenes, !cell_qc_results$discard] sce.Filtered
## class: SingleCellExperiment ## dim: 19938 2730 ## metadata(1): Samples ## assays(1): counts ## rownames(19938): ENSG00000175756 ENSG00000221978 ... ENSG00000169962 ENSG00000107404 ## rowData names(4): ID Symbol Type Chromosome ## colnames(2730): AAACCTGAGACTTTCG-1 AAACCTGGTCTTCAAG-1 ... TTTGTCAAGGACGAAA-1 ## TTTGTCAGTTCGGCAC-1 ## colData names(8): Sample Barcode ... subsets_Mito_percent total ## reducedDimNames(0): ## mainExpName: NULL ## altExpNames(0):